dplyr

[woneuy01]

library(tidyverse) library(dslabs) data(heights)

compute average and standard deviation for males

s <- heights %>% filter(sex == "Male") %>% summarize(average = mean(height), standard_deviation = sd(height))

access average and standard deviation from summary table

s$average s$standard_deviation

compute median, min and max

heights %>% filter(sex == "Male") %>% summarize(median = median(height), minimum = min(height), maximum = max(height))

alternative way to get min, median, max in base R

quantile(heights$height, c(0, 0.5, 1))

generates an error: summarize can only take functions that return a single value

heights %>% filter(sex == "Male") %>% summarize(range = quantile(height, c(0, 0.5, 1)))

[woneuy01]

https://rafalab.github.io/dsbook/tidyverse.html#the-dot-operator

[woneuy01]

The dot operator allows you to access values stored in data that is being piped in using the %>% character. The dot is a placeholder for the data being passed in through the pipe. The dot operator allows dplyr functions to return single vectors or numbers instead of only data frames. us_murder_rate %>% .$rate is equivalent to us_murder_rate$rate.

[woneuy01]

library(tidyverse) library(dslabs) data(murders)

murders <- murders %>% mutate(murder_rate = total/population*100000) ( **here total is murder number by states) summarize(murders, mean(murder_rate))

calculate US murder rate, generating a data frame

us_murder_rate <- murders %>% summarize(rate = sum(total) / sum(population) * 100000) us_murder_rate

rate 1 3.034555

extract the numeric US murder rate with the dot operator

us_murder_rate %>% .$rate

[1] 3.034555

calculate and extract the murder rate with one pipe

us_murder_rate <- murders %>% summarize(rate = sum(total) / sum(population * 100000) %>% .$rate

Note that an equivalent way to extract a single column using the pipe is us_murder_rate %>% pull(rate).

[woneuy01]

The group_by function from dplyr converts a data frame to a grouped data frame, creating groups using one or more variables.

summarize and some other dplyr functions will behave differently on grouped data frames. Using summarize on a grouped data frame computes the summary statistics for each of the separate groups.

[woneuy01]

Code

libraries and data

library(tidyverse) library(dslabs) data(heights) data(murders)

compute separate average and standard deviation for male/female heights

heights %>% group_by(sex) %>% summarize(average = mean(height), standard_deviation = sd(height))

A tibble: 2 x 3 sex average standard_deviation

1 Female 64.9 3.76 2 Male 69.3 3.61

compute median murder rate in 4 regions of country

murders <- murders %>% mutate(murder_rate = total/population * 100000) murders %>% group_by(region) %>% summarize(median_rate = median(murder_rate))

region median_rate

1 Northeast 1.80 2 South 3.40 3 North Central 1.97 4 West 1.29

[woneuy01]

libraries and data

library(tidyverse) library(dslabs) data(murders)

set up murders object

murders <- murders %>% mutate(murder_rate = total/population * 100000)

arrange by population column, smallest to largest

murders %>% arrange(population) %>% head()

arrange by murder rate, smallest to largest

murders %>% arrange(murder_rate) %>% head()

arrange by murder rate in descending order

murders %>% arrange(desc(murder_rate)) %>% head()

arrange by region alphabetically, then by murder rate within each region

murders %>% arrange(region, murder_rate) %>% head()

show the top 10 states with highest murder rate, not ordered by rate

murders %>% top_n(10, murder_rate)

show the top 10 states with highest murder rate, ordered by rate

murders %>% arrange(desc(murder_rate)) %>% top_n(10)

[woneuy01]

To ignore the NAs, we can use the na.rm argument:

mean(na_example, na.rm = TRUE) sd(na_example, na.rm = TRUE)

[woneuy01]

filter female and age "20-29"

library(dplyr) library(NHANES) data(NHANES)

fill in what is needed

head(NHANES)

ID SurveyYr Gender Age AgeDecade AgeMonths Race1 Race3 Education MaritalStatus HHIncome

1 51624 2009_10 male 34 " 30-39" 409 White NA High Sch… Married 25000-3… 2 51624 2009_10 male 34 " 30-39" 409 White NA High Sch… Married 25000-3… 3 51624 2009_10 male 34 " 30-39" 409 White NA High Sch… Married 25000-3… 4 51625 2009_10 male 4 " 0-9" 49 Other NA NA NA 20000-2… 5 51630 2009_10 female 49 " 40-49" 596 White NA Some Col… LivePartner 35000-4… 6 51638 2009_10 male 9 " 0-9" 115 White NA NA NA 75000-9…

tab <- NHANES %>% filter(AgeDecade == " 20-29" & Gender == "female")

[woneuy01]

Complete the line of code to save the average and standard deviation of systolic blood pressure as average and standard_deviation to a variable called ref.

Use the summarize function after filtering for 20-29 year old females and connect the results using the pipe %>%. When doing this remember there are NAs in the data!

library(dplyr) library(NHANES) data(NHANES)

complete this line of code.

ref <- NHANES %>% filter(AgeDecade == " 20-29" & Gender == "female") %>% summarise ( average = mean(BPSysAve, na.rm = TRUE), standard_deiation=sd(BPSysAve ,na.rm = TRUE))

[woneuy01]

Modify the line of sample code to assign the average to a numeric variable called ref_avg using the . or pull

library(dplyr) library(NHANES) data(NHANES)

modify the code we wrote for previous exercise.

ref_avg <- NHANES %>% filter(AgeDecade == " 20-29" & Gender == "female") %>% summarize( ref_avg = mean(BPSysAve, na.rm = TRUE), standard_deviation = sd(BPSysAve, na.rm=TRUE)) %>% .$ref_avg

[woneuy01]

Report the min and max values for the same group as in the previous exercises.

Use filter and summarize connected by the pipe %>% again. The functions min and max can be used to get the values you want. Within summarize, save the min and max of systolic blood pressure as minbp and maxbp

library(dplyr) library(NHANES) data(NHANES)

complete the line

NHANES %>% filter(AgeDecade == " 20-29" & Gender == "female") %>% summarise ( minbp = min(BPSysAve, na.rm = TRUE), maxbp = max(BPSysAve, na.rm = TRUE))

[woneuy01]

Use the functions filter, group_by, summarize, and the pipe %>% to compute the average and standard deviation of systolic blood pressure for females for each age group separately.

Within summarize, save the average and standard deviation of systolic blood pressure (BPSysAve) as average and standard_deviation

library(dplyr) library(NHANES) data(NHANES)

complete the line with group_by and summarize

NHANES %>% filter(Gender == "female") %>% group_by (AgeDecade) %>% summarise (average = mean(BPSysAve, na.rm=TRUE) , standard_deviation= sd(BPSysAve, na.rm=TRUE))

[woneuy01]

group_by(AgeDecade, Gender).

Note that we no longer have to filter!

library(NHANES) data(NHANES) NHANES %>% group_by(AgeDecade, Gender) %>% summarise( average = mean(BPSysAve, na.rm = TRUE), standard_deviation= sd(BPSysAve, na.rm=TRUE))

[woneuy01]

library(dplyr) library(NHANES) data(NHANES) NHANES %>% filter(Gender == "male" & AgeDecade==" 40-49") %>% group_by(Race1) %>% summarize(average = mean(BPSysAve, na.rm = TRUE), standard_deviation = sd(BPSysAve, na.rm=TRUE)) %>% arrange(average)